Virtual metrology system and method

ABSTRACT

A virtual metrology system and a method therefor are provided herein. In the system, a set of process data is gathered and clustered according to a plurality of predetermined patterns. The clustered set of process data is calculated according to the corresponding pattern, so as to obtain a comparison result. If the obtained result meets a desired output, a corresponding step is performed based on the result. In one case, the corresponding step is a normal sampling step if the clustered set of process data meets the corresponding pattern. If the clustered set of process data does not meet the corresponding pattern, an alarm is generated thereby, and the corresponding equipment may be shut down. In another case, the corresponding step is a maintenance, repair, and overhaul step if the clustered set of process data meets the corresponding pattern.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefit of Taiwan applicationserial no. 103126779, filed on Aug. 5, 2014. The entirety of theabove-mentioned patent application is hereby incorporated by referenceherein and made a part of this specification.

FIELD OF THE INVENTION

The invention relates to a virtual metrology (VM) system and a VMmethod. More particularly, the invention relates to a VM system and a VMmethod configured to cluster data according to a plurality ofpredetermined patterns and perform a corresponding step if the obtaineddata meet expectations.

DESCRIPTION OF RELATED ART

In the manufacturing-related industries, such as semiconductor anddisplay panel manufacturing industries, quality control of workpiecesproduced by process equipment is required for ensuring stability of themanufacturing performance of the equipment and improving production andyield. Virtual metrology (VM) is a method to conjecture quality of aprocess workpiece according to the process data of the process equipmentif no physical metrology operation is performed or can be performed onthe process workpiece. When the VM technology is applied, the physicalconditions or properties of chambers in one type of equipment or in oneequipment are unlikely the same; hence, to make sure the conjectureaccuracy, the conjecture model need be established according to thephysical conditions or properties of different chambers in theequipment. In order not to spent significant labor cost and other costson individually establishing respective models for different chambers ineach equipment, an automatic virtual metrology (AVM) server and a methodtherefor have been proposed.

According to the AVM technology, a VM value and a feedback correctionvalue are applied to ensure promptness and accuracy, e.g., the AVMprocess may be performed in a dual-phase manner. In phase I, theconjecture step is carried out by calculating the first-stage VM valueof a certain workpiece right after the process data collection of theworkpiece is completed, so as to satisfy the requirement for promptness.The Phase-II algorithm starts to collect the metrology data of arandomly selected workpiece in a cassette (for re-training ormodulation) and then recalculate the phase-II VM data of all workpiecesin the cassette to which the randomly selected workpiece belongs, so asto satisfy the requirement for accuracy. The AVM technology is alsoapplied to generate a global similarity index (GSI) and a reliance index(RI) of the phase-I VM value and the phase-II VM value to quantify thereliability of the conjectured VM value. The AVM method saves tremendoustime for introducing the VM algorithm to the chambers in the same typeof equipment or in the same equipment as well as maintaining accuracy ofreal-time VM.

Please refer to FIG. 1 which is a schematic diagram illustrating aconventional AVM technical scheme. Each of the AVM system components isconnected to the corresponding manufacturing equipment and metrologyequipment; for instance, the AVM server 116 shown in FIG. 1 is connectedto the corresponding manufacturing equipment 112 and metrology equipment114. The other AVM server 126 is connected to the correspondingmanufacturing equipment 122 and metrology equipment 124 The obtaineddata are transmitted to a model creation server 130, a VM managementserver 140, and a database 150 though a simple-object-access-protocol(SOAP) transmission interface, and the model creation server 130, the VMmanagement server 140, and the database 150 transmit data to and sharedata with remote monitoring systems 160 and 162 through the SOAPtransmission interface as well.

However, the existing VM schemes generally adopt the data drivenmethodology in different ways to ensure the correctness of theconjectured VM value. Such VM schemes often encounter the same problem,i.e., only issues arising from actual measurement can be recognized, andthe correctness of data need be ensured by making corrections throughfeedback paths. In case of any unrecognizable issue, the feedback pathsmay worsen the problems, and thus real-time correction becomesimpossible. Although a significant amount of the process data ofmanufacturing equipment may be collected, such data collection maymerely serve to monitor the quality of the produced workpieces.

SUMMARY OF THE INVENTION

The invention is directed to a virtual metrology (VM) system and a VMmethod. In the VM system, the obtained data of tool conditions areclustered according to a plurality of predetermined patterns. Theobtained data are calculated according to the patterns, so as to obtaina result. If the result meets expectations, a corresponding step isperformed.

According to an embodiment of the invention, the corresponding step is anormal sampling step if the result indicates the obtained data meet oneof the predetermined patterns. If the obtained data do not meet any ofthe predetermined patterns, an alarm is generated thereby, and thecorresponding equipment may be shut down or other measures may be taken.

According to an embodiment of the invention, the corresponding step is amaintenance, repair, and overhaul step if the result indicates theobtained data meet one of the predetermined patterns.

According to an embodiment of the invention, if the obtained data oftool conditions do not meet any of the predetermined patterns, the dataare stored; after certain amount of the data is accumulated, a newpattern is established, and the newly established pattern maydynamically serve as a basis for clustering data.

Several exemplary embodiments accompanied with figures are described indetail below to further describe the invention in details.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram illustrating a conventional automaticvirtual metrology (AVM) technical scheme.

FIG. 2 is a schematic diagram illustrating manufacturing environmentwhere a virtual metrology (VM) system is applied according to anembodiment of the invention.

FIG. 3 is a schematic diagram illustrating a VM system and a VM methodaccording to an embodiment of the invention.

FIG. 4A is a schematic diagram illustrating a VM system according toanother embodiment of the invention; FIG. 4B is a schematic diagramillustrating relevant processes.

FIG. 5 is a schematic flowchart illustrating a VM method according to anembodiment of the invention.

DETAILED DESCRIPTION OF DISCLOSED EMBODIMENTS

The invention is directed to a virtual metrology (VM) system and a VMmethod. In the VM system, the obtained data of tool conditions areclustered according to a plurality of predetermined patterns. Theobtained data are calculated according to the patterns, so as to obtaina result. If the result meets expectations, a corresponding step isperformed.

According to an embodiment of the invention, the corresponding step is anormal sampling step; that is, if the result indicates the obtained datameet one of the predetermined patterns, metrology is performed throughcarrying out the normal sampling step. For instance, the actualmetrology data of a randomly selected workpiece are obtained as thebasis for re-training or modulation, so as to satisfy the requirementfor accuracy. If the obtained data do not meet any of the predeterminedpatterns, an alarm is generated thereby, and the corresponding equipmentmay be shut down or other measures may be taken. In an embodiment of theinvention, if the obtained data do not meet any of the predeterminedpatterns, the data are stored and accumulated to a certain amount, and anew pattern for comparison is dynamically added.

In another embodiment of the invention, the corresponding step may be amaintenance step or a parts-replacing step. For instance, if the resultmeets one of the predetermined patterns, the maintenance step or theparts-replacing step is performed. If the result does not meet any ofthe predetermined patterns, the result is disregarded. However, if themanufacturing equipment is crashed because the maintenance step is notperformed, the corresponding equipment parameters or data are stored andaccumulated to a certain amount, and then a new pattern for comparisonis dynamically added. A process apparatus is production equipment havinga mechanical structure and various parts. The mechanical structurerequires maintenance because of the accumulation of bi-products duringproduction, and aging consumable parts require periodic replacement. Thepatterns can be associated with the accumulation state of bi-productsduring production or the aging state of the consumable parts.

According to the drawings, a VM system and a method therefor areprovided below according to an embodiment of the invention, while thedescriptions should not be construed as limitations to the invention.

FIG. 2 is a schematic diagram illustrating manufacturing environmentwhere a virtual metrology (VM) system is applied according to anembodiment of the invention. The manufacturing environment may includean environment for manufacturing semiconductors, automobiles, liquidcrystal display (LCD) panels, and so on. In the present embodiment, themanufacturing environment includes an on-site manufacturing system 210,an equipment control system 250, and a data storage system 220. Theon-site manufacturing system 210, the equipment control system 250, andthe data storage system 220 may be connected by communication networksfor data exchange, e.g., through public Internet, internal privatenetwork (Ethernet or local area network), or a combination thereof.

The on-site system 210 is configured to performing processes formanufacturing various workpieces, and the manufacturing processes may beonline/offline manual/automated manufacturing processes. Here, theon-site system 210 may include at least one manufacturing equipment 212,metrology equipment 214, other computer hosts, and other equipment thatmay be applied to complete the manufacturing processes. Through anautomated interface 216, the on-site system 210 may communicate with andexchange data with the equipment control system 250 and the data storagesystem 220.

The manufacturing equipment 212 is, for instance, an ion implanter, athermal reactor, an etcher, a lithography machine, and similar devicesused in the semiconductor manufacturing process. The metrology equipment214 may include ellipsometer, interferometer, scanning electronmicroscopy (SEM), or the like.

The automated interface 216 is connected to the manufacturing equipment212, the metrology equipment 214, and other computer hosts, so as toobtain the process data. According to an embodiment of the invention,the automated interface 216, the manufacturing equipment 212, and themetrology equipment 214 may be connected through a connection interfacefor data exchange. Here, the connection interface includes asemiconductor equipment communication standards (SECS) interface, ageneric model for communication and control of manufacturing equipment(GEM) interface, a SECS/GEM interface, and an equipment data acquisition(EDA) interface (i.e., interface A).

The on-site manufacturing system 210, the equipment control system 250,and the data storage system 220 communicate with each other and exchangedata. The data storage system 220 may include a database, a file system,or any other data stored in a non-volatile memory or a volatile memory;here, the non-volatile memory is a hard disc, a cassette player, anoptical storage medium, etc; the volatile memory may be a random accessmemory, for instance. In an embodiment of the invention, the datastorage system 220 can obtain data from a plurality of data storagesources, e.g., from an equipment maintenance data source, a metrologydata source, a process data source, and so on.

The data storage system 220 can store process data (e.g., manufacturingrecipes), such as temperature, pressure, the used chemical materials,manufacturing time, etc. In addition, the data storage system 220 storeshistorical maintenance data of the manufacturing equipment, data ofstocks, and so forth. The data storage system 220 also stores theprocess data obtained from the manufacturing equipment 212 of theon-site manufacturing system 210 or the metrology data obtained from themetrology equipment 214.

The process data may be process characterized parameters (e.g.,different physical conditions or properties) executed by themanufacturing equipment, and these parameters may be obtained by sensorsof the equipment and/or by operation parameters of the equipment and arecollectively referred to as the process data. Among the process data,the data obtained by the sensors of the equipment exemplarily includepressure of a chamber, temperature, RF power, or RF reflection power,for instance. By contrast, among the process data, the operationparameters of the equipment are predetermined flow rates (of chemicalreaction solvent, for instance), throttle valve settings (e.g., chemicalchamber exhaust vacuum pump settings), and so forth.

The metrology data may include a wafer thickness metrology value (e.g.,measured by an ellipsometer), a particle quantity metrology value (e.g.,measured by scanning electron microscopy, SEM), a wafer curvaturemetrology value (e.g., measured by an interferometer), and so forth, forinstance.

The equipment control system 250 may manage all or parts of operationsin the manufacturing factory; besides, the equipment control system 250may include online/offline manual/automated processes, and the controlcomputations and operations are done by calculators or host servers. Theprocesses, for instance, include a tracking and monitoring processperformed on equipment, a delivery process of materials, a laborscheduling and adjusting process, etc. The equipment control system 250may include a fault detection and classification (FDC) component 230 anda VM component 240.

The FDC component 230 may timely obtain data from the automatedinterface 216 of the on-site manufacturing system 210 and obtain datafrom the data storage system 220. The VM component 240 timely obtainsthe tools conditions of the manufacturing equipment or other datarequired by the VM method described herein through the automatedinterface 216 and obtain data from the data storage system 220. The FDCcomponent 230 is connected to the VM component 240, and VM models of theVM component 240 may be employed to conjecture or measure data accordingto other metrology values or process data.

In the VM system and the VM method provided herein, the obtained data oftool conditions are clustered according to a plurality of predeterminedpatterns. The obtained data are calculated according to the patterns, soas to obtain a result. If the result meets expectations, a correspondingstep is performed.

FIG. 3 is a schematic diagram illustrating a VM system and a VM methodaccording to an embodiment of the invention. In the present embodiment,the VM system at least includes a process apparatus, metrologyequipment, and a VM server. As shown in FIG. 3, the VM system providedin the present embodiment includes metrology equipment 310, a processapparatus 320, a data processing unit 330, a data processing unit 340,and a VM conjecture module 350.

In an embodiment of the invention, the manufacturing environment isprovided in the present embodiment with reference to FIG. 2, and themetrology equipment 310 or the process apparatus 320 can obtain therequired process data or metrology data required by the manufacturingequipment of the on-site manufacturing system or required by themetrology equipment.

The data processing unit 330 is connected to the metrology equipment 310to obtain the metrology data, the process data including manufacturingrecipes stored in the data storage system depicted in FIG. 2, thehistorical data maintained by the manufacturing equipment, or theaccumulated historical process data or metrology data obtained from themanufacturing equipment or the metrology equipment. Besides, the dataprocessing unit 330 serves to provide the VM conjecture module 350 withcalculation results (Y values) corresponding to the predeterminedpatterns. The Y values may be mean coefficients corresponding to theprocess data including a significant amount of manufacturing recipes, soas to obtain a conjectured value VP according to a computation formulawhile the corresponding process data are obtained. The conjectured valueVP is compared with the Y value to determine whether the predetenninedpatterns are met, which will be exemplified in the following embodimentwith reference to the drawings. Besides, the data processing unit 340 isconnected to the process apparatus 320, so as to obtain the process dataXi to Xn of the process apparatus 320.

The conjecture module 350 is a software module or a firmware module themay be executed by parts of the equipment of the equipment controlsystem 250; hence, no relevant descriptions are provided hereinafter,and the invention should not be limited thereto. Besides, the conjecturemodule 350 serves to gather the process data and cluster the processdata to obtain a plurality of data clusters. The data clusters arecompared with a plurality of patterns and are calculated according tothe patterns to obtain a result. If the result meets expectations, acorresponding step is performed.

In an embodiment of the invention, the conjecture module 350 of the VMsystem includes an auto cluster module 354 and a plurality of VMprocessors; in the present embodiment, the VM system includes N VMprocessors, and only VM processors 356 a, 356 b, and 356 c are providedherein for illustrative purposes. The conjecture module 350 obtains theprocess data Xi to Xn (e.g., the data obtained from the FDC component)of the equipment, and the obtained data are automatically clustered byan automatic clustering module 354, e.g., by selecting all or parts ofthe process data Xi to Xn required by each corresponding pattern.

The automatic clustering module 354 sends different data collections tothe corresponding N VM processors; for instance, the data Xa (e.g.,process data X1, X3, X5, and X7), the data Xb (e.g., process data X2,X4, X6, and X8), and the data Xc (e.g., process data X1, X5, X6, and X8)of the corresponding VM processors 356 a, 356 b, and 356 c are sent toobtain different VM outputs 358 a, 358 b, and 358 c. The data may betransformed to the output model 353 generated by the conjecture module350.

The conjecture module 350 may selectively include a physical model 352for making conjectures according to the obtained process data andgenerating an output model 351 based on the conjectures. The outputmodel 353 generated by the conjecture module 350 and/or the output model351 selectively generated by the conjecture module 350 may serve togenerate the reliance index (RI) of the VM value, so as to quantify thereliability of the conjectured VM value or send the data to thenext-stage equipment 360 for other purposes.

FIG. 4A is a schematic diagram illustrating a VM system according toanother embodiment of the invention; FIG. 4B is a schematic diagramillustrating relevant processes. As shown in FIG. 4A, the VM systemprovided in the present embodiment includes a plurality of VM conjecturemodules 420 to 428, and the data (i.e., the data X) relevant to themanufacturing process are transmitted from the process apparatuses 410to 418 to the corresponding VM conjecture modules 420 to 428. Besides,the VM conjecture modules 420 to 428 receive relevant data (i.e., thedata Y) from the process apparatus 430, so as to generate acorresponding comparison result.

With reference to FIG. 4B, steps in a VM method applicable to the VMsystem shown in FIG. 4A are illustrated. In step S402, the relevant dataY are input. In step S404, data X relevant to the predetermined patternsare selected from the obtained process data (i.e., the data X). In stepS406, the conjectured data Yp are generated according to the obtaineddata Y and the relevant data X. In step S408, the data Y and thegenerated conjectured value Yp are compared, if the comparison resultmeets the corresponding pattern, no further step is performed. However,if the comparison result does not meet the corresponding pattern, anerror message is displayed in step S410. According to an embodiment ofthe invention, if the comparison result meets the corresponding pattern,a normal sampling step is performed for measurement. If the comparisonresult does not meet any of the predetermined patterns, an alanii isgenerated thereby, and the corresponding equipment may be shut down orother measures may be taken.

FIG. 5 is a schematic flowchart illustrating a VM method according to anembodiment of the invention. In step S502, the process data of theequipment are gathered, e.g., the process data Xi to Xn are gathered,and n is an integer. In step S504, it is determined whether the processdata Xi to Xn are gathered completely; if not, the step S502 isrepeated. If the process data Xi to Xn are gathered completely, theprocess data Xi to Xn are clustered in step S506. The process data Xi toXn may be process characterized parameters (e.g., different physicalconditions or properties) executed by the manufacturing equipment, andthese parameters may be obtained by either sensors of the equipment orby operation parameters of the equipment or both, and are collectivelyreferred to as the process data. The data obtained by the sensors of theequipment exemplarily include pressure, temperature, RF power, or RFreflection power of the chamber, for instance. Among the process data,the operation parameters of the equipment are predetermined flow rates(of chemical reaction solvent, for instance), throttle valve settings(e.g., chemical chamber exhaust vacuum pump settings), and so forth.

After the process data Xi to Xn are completely clustered, in step S508,it is determined whether there is an existing model or pattern which ismet; if yes, the conjectured value VP corresponding to a certain patternis obtained by calculation. If there is no corresponding pattern, stepS512 is performed to make sure no conjectured value corresponding to thedata Y is output; in step S514, a corresponding step (e.g., issuing awarning alarm or temporarily stopping the equipment) is performed, andthe manufactured and produced workpiece is physically measured. Theobtained data and the measured result are respectively stored intoindividual sources; for instance, the data storage system depicted inFIG. 2 stores the process data (e.g., manufacturing recipes), such astemperature, pressure, the used chemical materials, manufacturing time,etc. Alternatively, the data storage system stores historicalmaintenance data of the manufacturing equipment, the measured data, andso forth. In step S518, it is determined whether the quantity of thestored data collections is sufficient to generate a new pattern. If thequantity of the stored data collections is insufficient, relevant dataare continuously gathered in step S524. If the quantity of the storeddata collections is sufficient, a new pattern is generated in step S520.For instance, if the number of patterns is N, the newly added patterncan be the (N+1)^(th) pattern. In step S522, the (N+1) patterns areactivated, so as to compare the patterns with the gathered data. In theVM system provided herein, patterns can be dynamically added in theaforesaid manner. After certain data collections not corresponding tothe predetermined patterns are accumulated, new patterns can bedynamically added, so as to ensure the accuracy of the VM system.

The predetermined patterns are obtained by performing multipleinductions on a plurality of manufacturing recipes (including theprocess data obtained from the same or similar past manufacturingprocesses) and corresponding metrology data to find a plurality ofmodels. For instance, in exemplary groups A, B, C, and D, differentprocess data have different mean coefficients, and the input data Y areobtained by defining the significantly accumulated historical data asfollows:Y=A1X1+A2X3+A3X5+A4X6+A5X7+A6X8+A0

Here, the conjectured value Yp may be obtained by calculating theprocess data X1, X3, X5, X6, X7, and X8, and the corresponding meancoefficients are A1, A2, A3, A4, A5, A6, and A0, which represents apattern.

The significantly accumulated historical data may be the obtainedmetrology data. For instance, the metrology data may include a waferthickness metrology value (e.g., measured by an ellipsometer), aparticle quantity metrology value (e.g., measured by scanning electronmicroscopy, SEM), a wafer curvature metrology value (e.g., measured byan interferometer), and so forth. The significantly accumulated processdata and the correspondingly obtained metrology data may be applied toestablish different models and thus generate different patterns.

As to the auto-classification technology, a chemical vapor deposition(CVD) process is performed for oxide thickness prediction in anembodiment of the invention, which will be explained hereinafter.

The process data Wj=(X1, X2, X3, . . . , X10) are obtained, wherein Wjis the j^(th) wafer sample, Xi is the FDC parameter corresponding to thej^(th) wafer sample, and in total there are N wafers. In the VM systemand the VM method provided in an embodiment of the invention, theprocess data (e.g., Wj=(X1, X2, X3, . . . , X10)) of equipment areobtained. Here, Wj is the j^(th) wafer sample, Xi is the FDC parametercorresponding to the j^(th) wafer sample, and in total there are Nwafers, whereby the data collections and the predetermined patterns areclustered. The patterns specifically meeting the manufacturing processor the equipment are obtained by perfoiming inductions on theaccumulated historical data; for instance, four data clusters with themean coefficients Ai, Bi, Ci, and Di are adopted.

Group A:Y=A1X1+A2X3+A3X5+A4X6+A5X7+A6X8+A0;

Group B:Y=B1X2+B2X3+B3X4+B4X5+B5X6+B6X7+B7X8+B8X9+B0;

Group C:Y=C1X1 +C2X3+C3X5+C4X6+B5X7+C0;

Group D:Y=D1X+B2X2+B3X4+B4X7+B5X8+B6X9+B7X10+B0

Through said equations, the corresponding conjectured value Yp can beobtained. If the obtained data meet one of the predetermined patterns,e.g., if the obtained data is compared with a predetermined thresholdvalue or a predetermined value, a normal sampling step is performed formeasurement.

According to the VM method described in an embodiment of the inventions,the corresponding step is a maintenance, repair or overhaul step if theresult meets one of the predetermined patterns. If the result does notmeet any of the predetermined patterns, the result is disregarded.However, if the manufacturing equipment is crashed because themaintenance repair or overhaul step is not performed, the correspondingequipment parameters or data are stored and accumulated to a certainamount, and then a new pattern for comparison is dynamically added. Theprocess apparatus is the production equipment having the mechanicalstructure and various parts. The mechanical structure requiresmaintenance because of the accumulation of bi-products duringproduction, and aging consumable parts require periodic replacement. Thepatterns can be associated with the accumulation state of bi-productsduring production or the aging state of the consumable parts.

Although the invention has been described with reference to the aboveembodiments, it will be apparent to one of ordinary skill in the artthat modifications to the described embodiments may be made withoutdeparting from the spirit of the invention. Accordingly, the scope ofthe invention will be defined by the attached claims and not by theabove detailed descriptions.

What is claimed is:
 1. A virtual metrology system at least comprising: aprocess apparatus comprising a set of process data, the processapparatus producing a workpiece according to the set of process data;metrology equipment configured to measure the workpiece and obtain anactual measured value; and a virtual metrology server configured togather the set of process data, duster the set of process data to obtaindata clusters, and compare the data clusters with patterns, wherein eachof the data clusters is defined by a set of coefficients comprising ofmean coefficients, each mean coefficient of the set of coefficients isrespectively multiplied by a corresponding value of the set of processdata to obtain individual values and the individual values are addedtogether to calculate a conjectured value, and each of the patternscorresponds respectively to the set of coefficients and a predeterminedvalue, wherein if the data clusters meet the patterns corresponding tothe data clusters by comparing the conjectured value of the dataclusters with the predetermined value corresponding respectively to thepatterns, performing a normal sampling step, and if the data clusters donot meet the patterns corresponding to the data clusters by comparingthe conjectured value of the data clusters with the predetermined valuecorresponding respectively to the patterns, performing an abnormalprocessing step that includes stopping the process apparatus.
 2. Thevirtual metrology system as recited in claim 1, wherein if the dataclusters do not meet the patterns corresponding to the data clusters,the virtual metrology server gathers the data clusters as bases foradding a number of the patterns.
 3. The virtual metrology system asrecited in claim 1, wherein the set of process data is obtained bysensors of the metrology equipment sensing the set of process data andby operation parameters of the metrology equipment.
 4. The virtualmetrology system as recited in claim 3, wherein manufacturing recipesobtained by the sensors of the metrology equipment comprise one of or acombination of pressure of a chamber, temperature, RF power, RFreflection power, a plurality of flow rates setting, and a plurality ofthrottle valve settings.
 5. The virtual metrology system as recited inclaim 1, wherein the patterns are obtained by performing multipleinductions on a plurality of manufacturing recipes and correspondingmetrology data to find models, and a comparison result is obtained bycalculating the set of process data as gathered through the models. 6.The virtual metrology system as recited in claim 5, wherein differentsets of process data have different mean coefficients through themodels, and a conjectured value is obtained therefrom.
 7. The virtualmetrology system as recited in claim 5, wherein the set of process datais obtained by sensors of the metrology equipment sensing the set ofprocess data and/or by operation parameters of the metrology equipment.8. The virtual metrology system as recited in claim 7, whereinmanufacturing recipes obtained by the sensors of the metrology equipmentcomprise one of or a combination of pressure of a chamber, temperature,RF power, RF reflection power, a plurality of flow rate settings, and aplurality of throttle valve settings.
 9. A virtual metrology method atleast comprising: obtaining a set of process data and producing aworkpiece according to the set of process data by a process apparatus;measuring the workpiece and obtaining an actual measured value; andobtaining the set of process data and clustering the set of process datato obtain data clusters, wherein each of the data clusters is defined bya set of coefficients comprising of mean coefficients, each meancoefficients of set of coefficients is respectively multiplied by acorresponding value of the set of process data to obtain individualvalues and the individual values are summed together to calculate aconjectured value, and each of the patterns corresponds respectively tothe set of coefficients and a predetermined value; and comparing thedata clusters with patterns, wherein if the data clusters meet thepatterns corresponding to the data clusters by comparing the conjecturedvalue of the data clusters with the predetermined value correspondingrespectively to the patterns, performing a normal sampling step, and ifthe data clusters do not meet the patterns corresponding to the dataclusters by comparing the conjectured value of the data clusters withthe predetermined value corresponding respectively to the patterns,performing an abnormal processing step that includes stopping theprocess apparatus.
 10. The virtual metrology method as recited in claim9, wherein if the data clusters do not meet the patterns correspondingto the data clusters, gathering the data clusters as bases fordynamically adding a number of the patterns.
 11. The virtual metrologymethod as recited in claim 9, wherein the set of process data isobtained by sensors of equipment sensing the set of process data and/orby operation parameters of the equipment.
 12. The virtual metrologymethod as recited in claim 9, wherein manufacturing recipes obtained bythe sensors of equipment comprise one of or a combination of pressure ofa chamber, temperature, RF power, RF reflection power, a plurality offlow rate settings, and a plurality of throttle valve settings.
 13. Thevirtual metrology method as recited in claim 9, wherein the patterns areobtained by performing multiple inductions on a plurality ofmanufacturing recipes and corresponding metrology data to find models,and a comparison result is obtained by calculating the set of proessdata as gathered through the models.
 14. The virtual metrology method asrecited in claim 13, wherein different sets of process data havedifferent mean coefficients according to the models, and a conjecturedvalue is obtained therefrom.
 15. The virtual metrology method as recitedin claim 13, wherein the manufacturing recipes are obtained by usingsensors of equipment sensing the set of process data and/or by operationparameters of the equipment.
 16. The virtual metrology method as recitedin claim 15, wherein the manufacturing recipes obtained by using thesensors of equipment comprise one of or a combination of pressure of achamber, temperature, RF power, RF reflection power, a plurality of flowrate settings, and a plurality of throttle valve settings.